Video display method, video display device, and non-volatile storage medium

Abstract

A video display method, a video display device, and a non-volatile storage medium. Considering that factors such as the brightness, display area, and display shape of a static image also affect the surface temperature of a panel, a video display method for effectively mitigating burn-in is provided. The video display method comprises: splitting a frame corresponding to an input video signal into multiple regions, detecting static image regions, calculating grayscale values of the static image regions, classifying adjacent regions of each grayscale into a same group, changing the weighting of coefficients on the basis of the area and / or shape of the grouped regions, and on the basis of the weighted coefficients, controlling the level of a video signal displayed as a static image.

Description

Video display method, video display device, and non-volatile storage medium

[0001] Cross-reference of related applications

[0002] This application claims priority to Japanese Patent Application No. 2024-228771, filed on December 25, 2024, entitled "Visual Display Method, Apparatus, and Visual Display Program", the entire contents of which are incorporated herein by reference. Technical Field

[0003] This application relates to video display methods, video display devices, and non-volatile storage media. Background Technology

[0004] In OLED displays, if the same image (such as a logo, an autoreflective slide projector, or a time display image) is displayed for an extended period in the same area of ​​the screen, the organic material in that area will degrade over time. As a result, if other images are displayed in the same area where the static image was displayed for a long time, the previously displayed static image will appear as a shadow or afterimage. This phenomenon is known as "burn-in," and various methods to mitigate burn-in have been employed in the past.

[0005] As a method to mitigate burn-in, one approach involves detecting areas where a static image is displayed for an extended period and reducing the brightness of those areas. This method, for example, divides a 2048 (=64×32) pixel image into multiple regions and detects the average brightness of each region. Furthermore, regions where the average brightness between frames remains unchanged over a long period are identified as static image regions. Then, while the static image in these regions remains static and has high brightness, control is applied to reduce the brightness of that image.

[0006] The control method of this display device determines that when images such as logos or automatic reflective projectors are displayed, they are static images and performs control for reducing scorching.

[0007] Furthermore, according to Patent Document 1, it is also known that scorching occurs more rapidly when the temperature of the pixels of the display is higher, and there is also a control device that incorporates temperature information into the determination factor of scorching reduction.

[0008] Existing technical documents

[0009] Patent documents

[0010] Patent document 1: Japanese Patent Application Publication No. 2007-286375. Summary of the Invention

[0011] As mentioned above, OLED displays require control devices to reduce burn-in, but there is a desire for an efficient image display method with further benefits.

[0012] In this embodiment, considering that (a) the higher the panel surface temperature, the greater the degree of scorching, and (b) factors such as the brightness of the static image, the display area of ​​the static image, and the display shape of the static image also affect the panel surface temperature (and thus the degree of scorching), the aim is to provide a video display method, apparatus, and program that can more effectively reduce scorching.

[0013] According to one embodiment, a video display method is provided, which divides a frame corresponding to an input video signal into multiple blocks, detects static image blocks between the beginning and end of the frame's time axis, calculates the grayscale value of the static image blocks, separates them into a predetermined range, groups adjacent static image blocks according to the range, changes the weighting of coefficients based on the minimum area of ​​the grouped regions, and controls the level of the displayed video signal based on the weighted coefficients. Attached Figure Description

[0014] Figure 1 is a configuration diagram of the video display device according to this embodiment;

[0015] Figure 2 is an explanatory diagram illustrating an example of the basic idea in implementing this embodiment;

[0016] Figure 3 is an explanatory diagram illustrating another example of the basic idea in implementing this embodiment;

[0017] Figure 4A is an explanatory diagram that schematically shows the signal processing performed by the frame divider in the signal processing of the video display device of Figure 1.

[0018] Figure 4B is an explanatory diagram that schematically shows the signal processing status of the static image block detector in the signal processing of the video display device of Figure 1.

[0019] Figure 4C is an illustrative diagram that schematically shows an example of the grayscale value calculation / grouping unit in the signal processing of the video display device of Figure 1, which detects the range of grayscale values ​​and groups them according to the range.

[0020] Figure 5A is an explanatory diagram showing multiple static image regions formed by static image blocks, and the number (area) of blocks in each region.

[0021] Figure 5B is an explanatory diagram showing multiple regions in the static image region of Figure 5A containing blocks with a grayscale value of 50% or higher, and showing the number (area) of blocks in each region.

[0022] Figure 5C is an explanatory diagram showing multiple regions in the static image area of ​​Figure 5A or Figure 5B where there are blocks with a gray value of 75% or higher, and showing the number (area) of blocks in each region.

[0023] Figure 5D is an illustrative diagram showing an example of how square areas, as indicated by thick dashed lines, are ensured within regions where there are blocks with a grayscale value of 50% or higher (the same applies to Figure 5B).

[0024] Figure 5E is an illustration of the state in which the square is moved in a state of further overlapping squares, for the parts that can be secured by the regions 731, 732, 734, 735, 736, and 737 of Figure 5D, and the numbers representing the length of the side of the square are assigned to each block that can secure the square.

[0025] Figure 5F is an illustrative diagram illustrating an example of how a coefficient calculator generates counts.

[0026] The attached figures are labeled as follows: 101…video signal input circuit, 102…time adjuster, 103…video brightness correction circuit, 104…OLED display, SA1…video signal importer, SA2…frame divider, SA3…static image block detector, SA4…grayscale value calculator / grouper, SA5…area calculator, SA6…shape detector, SA7…coefficient calculator, SA8…switcher. Detailed Implementation

[0027] The embodiments are described below with reference to the accompanying drawings.

[0028] <Example of Implementation>

[0029] Figure 1 illustrates an example of the configuration of the video display device and video display method of this application. The video signal input circuit 101 receives video signals from a tuner receiving broadcast waves (not shown) and a communication device for connecting to the Internet, and sends the video signals to the time adjuster 102 and the splitter SA2 included in the control device 200. The time adjuster 102 adjusts the timing so that the timing at which the control device 200 processes the frames of the display object, calculates coefficients for brightness correction, and sends the calculated coefficients to the video brightness correction circuit 103 coincides with the timing at which the frames of the display object sent from the video signal input circuit 101 are sent to the video brightness correction circuit 103.

[0030] The image brightness correction circuit 103 controls the brightness contained in the image signal of the static image area displaying a still image, such as a logo or autoreflective slide projector, in response to a coefficient sent by the coefficient calculator SA7, which will be described hereafter. The image signal output from the image brightness correction circuit 103 is input to the OLED display 104.

[0031] The control device 200 includes: a frame segmenter SA2 that divides a frame into multiple blocks; and a still image block detector SA3 that detects whether the image within each block is still or nearly still. It further includes a grayscale value calculator / grouper SA4, which can detect the grayscale values ​​of the still image blocks individually and, for example, group the still image blocks according to a grayscale value range above a specified grayscale value. The grouping information is sent to an area calculator SA6. The area calculator SA5 performs calculations using a unique method that represents the area of ​​each grouped still image region as the number of blocks (described later with reference to Figures 5A, 5B, and 5C). The area information from the area calculator SA5 is sent to a coefficient calculator SA7 and a shape detector SA6. The shape detector SA6 receives the grouping information that the blocks above the specified grayscale value range have been grouped, as well as the aforementioned area information. Then, the shape detector SA6 fits, for example, square blocks in each grouped block region, using the number of blocks on one side of the square as shape information. Further fitting of the square blocks, either by offsetting and fitting the square, or by reducing the size of the square and fitting it, uses a unique technique (details are explained later with reference to Figures 5D and 5F).

[0032] The functions and actions of each part are further explained below with reference to the diagram.

[0033] As shown in Figure 4A, frame divider SA2 sets one frame 700 to be divided into multiple blocks. In the number of divisions, if the number of horizontal and vertical pixels is set as X and Y, then the number of horizontal divisions = (number of horizontal pixels X / integer X1), and the number of vertical divisions = (number of vertical pixels Y / integer Y1). X1 and Y1 can be arbitrarily set according to the design. Preferably, X1 and Y1 are selected in a way that makes one block approximately square.

[0034] The still image block detector SA3 uses frames that have been divided into multiple blocks from the frame segmenter SA2 to detect still image blocks. For example, the still image block detector SA3 compares the brightness of corresponding blocks between frames output from the frame segmenter SA2 at a specified time t and frames output from the frame segmenter SA2 at a time t-1 earlier than the specified time t.

[0035] When comparing the brightness of corresponding blocks, if the brightness difference is within a specified value (e.g., 10%, but this can be changed depending on the design, etc., and is not limited to this), the static image block detector SA3 determines that the block is a static image block. When a block is determined to be a static image block, static image block identification information (block information or block address, etc.) is assigned to the block. It should be noted that other methods for detecting static images include: using the output of an activity detector to determine inactive image regions (blocks) as static images. Additionally, there are methods that utilize the output of an existing detector in a video display device that already has an activity detector.

[0036] The grayscale value calculator / grouper SA4 calculates the grayscale value of each still image block and separates the blocks into specified ranges based on the calculated grayscale values ​​(see Figure 4C, for example). That is, for each range, adjacent still image blocks in the same range are grouped together.

[0037] The separated ranges can be considered as follows: 75% or more (e.g., high grayscale value) (static image block 701 in Figure 4C), 50% or more (e.g., medium grayscale value) (static image block 702 in Figure 4C), and 0% or more (e.g., low grayscale value) (static image block 703 in Figure 4C) can be set as ranges ("%" represents the ratio of white to black in the block; more white means a higher ratio, and more black means a lower ratio). In separating the grayscale value ranges, it is determined whether the detected grayscale value is 75% or more. If it is, it is determined to be a high grayscale value. If it is not 75% or more, it is determined whether the grayscale value is 50% or more. If the grayscale value is determined to be 50% or more, it is determined to be a medium grayscale value; if it is determined to be less than 50%, it is determined to be a low grayscale value. These high, medium, and low grayscale value ranges are called grayscale value range information. Figure 4C shows the static image block after being divided into ranges according to grayscale values.

[0038] As described above, the grayscale value calculator / grouper SA4 calculates the grayscale value of each block of the static image by referring to the block information that has been determined to be a static image block, and performs grouping according to the range of grayscale values.

[0039] There are several ways to divide and group data according to the range of grayscale values. Several methods are described below.

[0040] <Explanation of methods for efficiently detecting the range of grayscale values>

[0041] For example, as shown in Figure 4C, the settings are divided into 75% or more (e.g., high grayscale value) (static image block of reference 701 in Figure 4C), 50% or more (e.g., medium grayscale value) (static image block of reference 702 in Figure 4C), and 0% or more (e.g., low grayscale value) (static image block of reference 703 in Figure 4C).

[0042] In this case, firstly, blocks with low grayscale values ​​are detected (corresponding to the detection of static image blocks (Figure 5A)). Next, block detection is performed based on the grayscale values ​​while referring to the information of the static image blocks in Figure 5A (e.g., block address, block grayscale value information). Next, blocks with high grayscale values ​​are detected by referring to the information of the blocks in the grayscale values ​​(e.g., block address, block grayscale value).

[0043] Thus, if the processing is performed in the order of detecting blocks with low grayscale values, blocks with medium grayscale values, and then blocks with high grayscale values, an efficient processing method is obtained as follows. That is, because the blocks are grouped in a way that the grayscale value is 50% or higher (including groups with grayscale values ​​of 75% or higher), and then groups with grayscale values ​​of 75% or higher, the number of blocks that confirm the grayscale value range gradually decreases with the number of separations, resulting in efficiency. However, the method of separating grayscale value ranges is not limited to this method.

[0044] For example, it can also be done as follows: while scanning the blocks of the static image region shown in Figure 5A from the upper left block of the frame to each block of the first (initial) row, each block of the second row, ..., each block of the bottom row and the final block of the lower right of the frame, group recognition is performed (which can also be called a grouping method of multiple grayscale value ranges by one frame scan).

[0045] In addition, as other grouping methods, there is a method that detects the high gray value group 701, the medium gray value group 702, and the low gray value group 703 by repeatedly scanning at different times (which can also be called a grouping method based on multiple frame scans of multiple gray value ranges).

[0046] <Explanation of a method for grouping multiple grayscale value ranges based on a single frame scan>

[0047] The static image block detector SA3 detects the region of the static image block shown in Figure 4B and provides the static image block information that can identify the static image block to the grayscale value calculator / grouper SA4.

[0048] The grayscale value calculation / grouper SA4 scans the blocks to the left of the first block in the first row of the frame, determining the grayscale value and its range for each block. Once the range of each block is determined, the grayscale value range is used as the attribute information for each block, and grayscale value identification information is added to the block information.

[0049] Once the grayscale value range determination for the blocks in the first (initial) frame is complete, the grayscale value range determination for the blocks in the second frame is performed. This process continues until the grayscale value range determination for the blocks in the second, third, and so on, up to the final frame.

[0050] After determining the grayscale value range of each block, the grayscale value calculator / grouper SA4 adds grayscale value recognition information based on the range information as attribute information to the block recognition information of each block. As a result, as shown in Figure 4C, information for groups 701, 702, and 703 can be obtained.

[0051] <Explanation of a grouping method for multiple grayscale value ranges based on multiple frame scans>

[0052] <Formation of group 701 based on high grayscale value blocks from the first scan>

[0053] For example, as shown in Figure 4C, the grayscale value calculation / grouper SA4 detects the range of grayscale values ​​of block B(1,1) (address (X,Y) = (1,1)) on the left side of the frame (also known as the frame). Currently, if the grayscale values ​​of block B(1,1) are set to be in the range of high grayscale values, then block B(1,1) is identified as group 701 with high grayscale values. Next, the range of grayscale values ​​of the adjacent block B(2,1) (address (X,Y) = (2,1)) to the right of block B(1,1) is detected. When the grayscale values ​​of block B(2,1) (address (X,Y) = (2,1)) are detected to be in the range of high grayscale values, the grayscale value calculation / grouper SA4 also identifies block B(2,1) as group 701 with high grayscale values.

[0054] In this way, the grayscale value calculation / grouper SA4 will detect the range of grayscale values ​​of the right neighboring block and group (identify) the block according to the range of grayscale values ​​until the block located at the right end of the screen is reached.

[0055] When the grayscale value calculation / grouper SA4 detects the range of grayscale values ​​up to block B(X,Y) located at the right end of the screen (X,Y varies depending on the size of the screen, number of pixels, number of blocks, etc.), it detects the range of grayscale values ​​of block B(1,2) (address (X,Y) = (1,2)) located at the left end of the second row from the top of the screen. For example, when block B(1,2) is in the range of high grayscale values, block B(1,2) is identified as a group with high grayscale values ​​701.

[0056] Then, similarly, the range of grayscale values ​​is detected sequentially for each block in the second row from the top of the image, identifying blocks with high grayscale values ​​and classifying them as group 701. When the detection of blocks with high grayscale values ​​in all blocks of the second row is complete, the detection of blocks with high grayscale values ​​in all blocks of the third row is performed. In this way, blocks with high grayscale values ​​in all blocks of the bottom row of the image can be detected, and the group of blocks with high grayscale values ​​is identified as group 701. This group 701 can also be referred to as, for example, grayscale value range identification information (or group identification information).

[0057] If the range of gray values ​​of the static image block being judged is a range of gray values ​​that is lower than the range of gray values ​​of the static image blocks judged so far (such as gray value middle (group 702), gray value low (group 703), etc.), the same method as described above shall be used to group them.

[0058] <Formation of group 702 of blocks in grayscale values ​​based on the second scan>

[0059] The grayscale value calculation / grouper SA4 is set to begin grouping blocks with a grayscale value greater than 50% (medium grayscale value) into group 702. Since the grayscale value calculation / grouper SA4 has already grouped the high grayscale value group 701, it begins detecting the range of grayscale values ​​for an ungrouped block, such as block B(1,8) (address (X,Y) = (1,8)). When block B(1,8) is detected as having a medium grayscale value, it is identified as group 702. Next, the range of grayscale values ​​for the right-neighboring block B(1,9) is detected. In the example shown in the figure, since block B(1,9) is also having a medium grayscale value, it is also identified as group 702.

[0060] Subsequently, similar to the case where group 701 was generated earlier, the range of grayscale values ​​for each block in the first row, the second row, ..., the final row is detected to form group 702 in terms of grayscale values. In this case, since the blocks in group 701 have already been grouped, grayscale values ​​for these blocks are set not to be detected in order to improve processing efficiency.

[0061] <Formation of group 703 based on low grayscale value blocks from the third scan>

[0062] In the case of forming a group 703 of blocks with low grayscale values, the group 703 is formed in the same way as in the case of forming a group 701 of blocks with high grayscale values ​​and a group 702 of blocks with medium grayscale values.

[0063] <Explanation on determining and handling isolated grayscale value ranges when grouping grayscale value ranges>

[0064] The grayscale value range determination process is further explained below. In blocks assigned static image block information, there are cases where adjacent blocks have different detected grayscale value ranges. In this case, a determination is made as to whether a block with a different grayscale value range is adjacent to a block with the same grayscale value range as the different grayscale value range. For example, because block (3, 5) in Figure 4C is adjacent to block B (3, 4) with the same grayscale value range, it is determined to be group 701 of the same grayscale value range with higher grayscale values. However, in the case where they are not adjacent (e.g., block B (4, 7) in Figure 4C (address (X, Y = (4, 7))), it is determined to be a group of other grayscale value ranges. By performing this process, the grayscale value calculation / grouping unit SA4 can detect static image regions within each range as shown in Figures 5A to 5C.

[0065] There are multiple cases where the regions are grouped as described above. That is, in the example of FIG4C, group 701 exists in multiple locations within one frame 700, and groups 702 and 703 also exist in multiple locations within one frame 700.

[0066] The grayscale value calculator / grouper SA4 sends the grayscale value range information (high, medium, or low) of each grouped block and the group information to which each block belongs to to the area calculator SA5.

[0067] <Instructions related to the area information generated by the area calculator SA5>

[0068] The SA5 area calculator utilizes the group information to which each block belongs and the range information of its grayscale values ​​(high, medium, or low). Furthermore, the SA5 area calculator can calculate the area of ​​a region grouped based on its grayscale value range. For example, the number of blocks within each region can also be used as area information for that region.

[0069] Figure 5A is a diagram representing static image blocks with gray values ​​greater than 0% (including high, medium, and low gray values). The figure shows an example where the static image region exists in three locations within the frame (regions A, B, and C). Furthermore, the area information for region A is shown as 204, the area information for region B as 122, and the area information for region C as 60; these values ​​represent the number of blocks.

[0070] Figure 5B shows an example of a composite region formed by merging regions with low grayscale values ​​and regions with high grayscale values. This example also shows instances where the composite region is scattered across multiple locations, with the values ​​recorded in each region representing the number of blocks, i.e., area information.

[0071] Figure 5C shows an example of a region with only high grayscale values. In this example, it illustrates that regions with high grayscale values ​​exist in multiple locations, and the values ​​recorded in each region are the number of blocks, i.e., area information.

[0072] Furthermore, as a method for detecting grayscale values ​​and grouping, there is also the following method: preparing grayscale value range detection circuits (detection circuits for low, medium, and high grayscale values) in parallel with the number of groups, and obtaining group information after simultaneous grouping.

[0073] As mentioned above, it can be said that the area information representing the area utilizes the number of blocks in the block region grouped according to the range of gray values.

[0074] Then, the area calculator SA5 sends the area information shown in Figures 5A, 5B, and 5C as supplementary information to the block identification information to the coefficient calculator SA7 and the shape detector SA6.

[0075] <Shape detection of each region performed by shape detector A6>

[0076] Shape detector SA6, referring to area information sent from area detector SA5, obtains shape information for each region with a grayscale value larger than a specified grayscale value. In this case, within each region, the region with the largest square area is identified, and information on the number of blocks constituting one side of that square is obtained. Furthermore, shape detector SA6 defines regions with successively smaller square areas and obtains information on the number of blocks constituting one side of that square as shape information.

[0077] As described above, the shape detector SA6 acquires shape information for each region with a gray value larger than a specified gray value. Specifically, within each region, it acquires information about the number of blocks on one side of the square that can guarantee the largest area at that location. If residual areas remain, it shifts the square or gradually reduces its size. If the size has been reduced, it calculates the number of blocks on one side of the smaller square. It should be noted that the shape is not limited to a square; other shapes (pentagons, hexagons, etc.) are also possible.

[0078] Figure 5D illustrates, as an example, the process by which the shape detector SA6 detects the shape of a static image region formed by blocks with grayscale values, for example, greater than 50% (see Figure 5B). Figure 5E shows an example of assigning the number of blocks on one side of a square as shape information to each block after a square is detected. Thus, the number of blocks on one side of a square is used in the coefficient calculator SA7 as part of the calculation element for determining the coefficient controlling the brightness of the corresponding block. It should be noted that a specific example of shape detection will be further described in detail later. The shape detector SA6 sends the acquired square region information to the coefficient calculator SA7.

[0079] The coefficient calculator SA7 has a storage unit (not shown) that stores coefficients for controlling the brightness of the video signal used by the video brightness correction circuit 103. These coefficients are determined, for example, based on the area information of the static image region and the shape of each gray value larger than a predetermined gray value calculated by the shape detector SA6. This operation will be explained later in Figure 5F. Alternatively, the coefficients can also be determined using either the area information of the static image region or the shape of the static image region. The determined coefficients are then sent as control signals to the video brightness correction circuit 103.

[0080] Figure 5F shows an example of calculating the weighting coefficients of a single block using the coefficient calculator SA7. A conversion table 910 for weighting coefficients based on grayscale range, a conversion table 911 for weighting coefficients based on area, and a conversion table 912 for weighting coefficients based on shape are pre-set in the coefficient calculator SA7. For each grayscale range, based on the area and shape of the actual static image region, the corresponding coefficient is multiplied according to the conversion table, and finally, the largest weighting coefficient is applied. The conversion tables for weighting coefficients depending on grayscale, area, and shape can be determined based on temperature evaluation.

[0081] In the conversion table 910 based on the weighting coefficients of the grayscale range, the following settings are made:

[0082] When the grayscale range is above 0% and below 50%, the coefficient is 1.

[0083] When the grayscale range is above 50% and below 75%, the coefficient is 5.

[0084] When the grayscale range is above 75%, the coefficient is 15.

[0085] In the area-based weighting coefficient conversion table 911, the following is set:

[0086] When the number of blocks is 1, the coefficient is 1.

[0087] When the number of blocks is 2 to 9, the coefficient is 2.

[0088] When the number of blocks is 10-29, the coefficient is 4.

[0089] When the number of blocks is 30-49, the coefficient is 6.

[0090] When the number of blocks is 50-99, the coefficient is 7.

[0091] When the number of blocks is 100, the coefficient is 8.

[0092] In the shape-based weighting coefficient conversion table 912, the coefficient is set to 1 when there are 1 side, 2 when there are 2 side, 3 when there are 3 side, 4 when there are 4 side, 5 when there are 5 side, 6 when there are 6 side, 7 when there are 7 side, 8 when there are 8 side, 9 when there are 9 side, and 10 when there are 10 side.

[0093] In the above configuration, a switcher SA8 for correcting coefficients can also be further provided. For example, considering that the rise in panel surface temperature is related to the degradation of OLED elements, the operating environment of the OLED display 104 can also be taken into account. For example, if a sensor for detecting ambient brightness, commonly used in televisions, is provided, the information from this sensor can be used to correct the coefficients. Furthermore, if the television is connected to the internet, date, temperature, and time information can be obtained from the internet to determine the season, temperature, daytime, and nighttime conditions during use, and the switcher SA8 can then correct the coefficients. Additionally, if the television has a temperature sensor, the switcher SA8 can use either the information from that temperature sensor or the information from a temperature sensor in another device via network communication to correct the coefficients.

[0094] <Characteristic shape detection method using shape detector SA6>

[0095] The method for obtaining the characteristics of the weighted coefficients performed by the shape detector SA6 described above is explained. In Figure 5E, for the portions secured by the square regions 731, 732, 734, 735, 736, and 737 obtained in Figure 5D, numbers representing the length of the sides of the square are assigned to each block that can form a square even when the squares overlap. When the largest square with 5 vertical blocks and 5 horizontal blocks can be formed (refer to the portion with reference numeral 731 in Figure 5D), 5 is recorded in each block. In addition, when the number of vertical blocks is 4 and the number of horizontal blocks is 4 (refer to the portion with reference numeral 736 in Figure 5D), 4 is recorded in each block. However, when a 5×5 square block overlaps with a 4×4 square block, the 5×5 square block (refer to the portion of square region 731 in Figure 5D) is prioritized (the larger area is prioritized), and only 4 squares are allocated in the overflowing blocks where the 5×5 square block is prioritized (refer to the portion marked region 810 in Figure 5E) (refer to the portion marked region 811 in Figure 5E). The above describes an example of obtaining shape information for the portions marked regions 810 and 811 in Figure 5E; however, the same shape detection method is used to obtain shape information in other block regions. In the frame of Figure 5E, the region marked "3, 3, 1" on the right, the region marked "3, 4, 2" on the lower right, and the region marked "4, 2, 1" on the lower left are obtained using the same steps as the method for obtaining the shape information of regions 810 and 811.

[0096] As described above, the weighting coefficient information for each static image region can be determined in the coefficient calculator AS7 based on the grayscale value of each static image region, the area information of each range of grayscale values ​​of the static image region distinguished by grayscale values, and the shape information of the static image region. In the image brightness correction circuit 103 (see Figure 1), image signal reduction processing is performed based on the above coefficient information. Here, the brightness of each block in each region is controlled for each block as illustrated in Figure 5F.

[0097] It can be said that, in the shape information representing the shape of the grouped region as described above, the number of blocks forming a fitted specified shape (e.g., a square) within the range of specified gray values ​​above the specified gray value is utilized.

[0098] <Explanation of the basis for considering the area, shape, etc. of the same group of regions>

[0099] Figures 2 and 3 are shown to illustrate the basic reasons for including the area calculator SA5 and the shape detector SA6 in this embodiment.

[0100] Figure 2 shows three display panels 301, 302, and 303. A small square image 311 is displayed in the center of display panel 301 on the left side of the figure, a large square image 312 is displayed in the center of display panel 302 in the center of the figure, and a long rectangular image 313 in the horizontal direction is displayed in the center of display panel 303 on the right side of the figure.

[0101] Here, the small square image 311, the large square image 312, and the rectangular image 313 are all set to have the same brightness. In addition, the large square image 312 and the rectangular image 313 are set to have the same area.

[0102] When the above settings are applied, comparing the panel surface temperatures of each image area of ​​the small square image 311, the large square image 312, and the rectangular image 313 after a certain period of time yields the following results. If expressed in terms of large, medium, and small, then...

[0103] Large square image 312...high panel surface temperature

[0104] Rectangular image 313······ Panel surface temperature

[0105] Small square image 311... low panel surface temperature.

[0106] Based on the above results, it can be seen that even with the same brightness, differences in area and shape result in variations in panel surface temperature within static image areas. This means that even with the same brightness, multiple static image areas such as logos will exhibit different degrees of component degradation (the degree of burning) depending on their area and shape.

[0107] In this embodiment, the coefficient calculator SA7 shown in FIG1 weights the coefficients controlling the brightness (grayscale value) of the video signal (signal of the static image region) passing through the video brightness correction circuit 103. In this case, the coefficient calculator SA7 adjusts the weighting based on the grayscale value of the static image region and at least a combination of area or shape.

[0108] Figure 3 illustrates an example of the basic idea behind how the weighting of the above coefficients varies depending on the shape and area of ​​the static image region.

[0109] Now, suppose the display panel is divided into 7×7 blocks. The explanation is done with columns in the vertical direction and rows in the horizontal direction. An example is shown where the first column in the vertical direction considers area-based weighting information, the second column considers shape-based weighting information, and the third column considers the coefficients resulting from the weighting of both area and shape.

[0110] The first column shows the differences in area; let's explain the first column first.

[0111] In Example 441 of Column 1, the block within the static image area is 1 (area 1), and this area 1 is used as a weighted reference value. In Example 442, the same group of areas within the static image area consists of 4 horizontal and 4 vertical blocks (area 16), and this area 16 is used as a weighted reference value. In Example 443, the same group of areas within the static image area consists of 5 horizontal and 3 vertical blocks (area 15), and this area 15 is used as a weighted reference value.

[0112] Column 2 illustrates the different shapes. Example 451 in Column 2 is a small square, where the number of sides (1) is used as a weighted reference value. Example 452 is a large square, where the number of side blocks (4) is used as a weighted reference value. Example 453 is a rectangle, but, as indicated by the thick lines, it is a square, and the number of side blocks (3) is used as a weighted reference value. This is because, as explained in Figure 2, the square portion is used because the surface temperature of the panel is strongly affected by the square portion. Therefore, in Example 453, the number of side blocks (3) is used as a weighted reference value.

[0113] The shape information mentioned above can also be used directly as weighted information.

[0114] Next, column 3 shows the final weighted coefficients in each example, based on the information combining area and shape. In examples 441 and 451, the coefficient 1 was obtained by multiplying the number of area blocks (1) and the number of blocks on one side of the square (1). In examples 442 and 452, the coefficient 64 was obtained by multiplying the number of area blocks (16) and the number of blocks on one side of the square (4). In examples 443 and 453, the coefficient 45 was obtained by multiplying the number of area blocks (15) and the number of blocks on one side of the square (3).

[0115] As shown in Figure 1, the coefficient calculator SA7 provides the aforementioned coefficients to the image brightness correction circuit 103 as control elements for the image signal level of each block. In this case, for example, the larger the value of the coefficient, the lower the image signal level is.

[0116] In the above description, both area-based weighting and shape-based weighting were used. However, even if either one is used, that is, only area-based weighting or only shape-based weighting can be used, the effect of this embodiment can be obtained.

[0117] It should be noted that, in order to perform the above-described data processing, a random access memory (RAM) is provided within the control device 200 for forming and temporarily storing the various information described in Figures 5A-5F. Furthermore, the RAM stores a program (i.e., a video display program) for performing the aforementioned actions of the control device 200. Additionally, the RAM manages, for example, block address data, block summaries (i.e., static image area data) (Figure 5A), grayscale recognition data for identifying blocks within the static image area (Figures 5B and 5C), and area recognition data (Figure 5E). There is also program memory storing programs for constructing the aforementioned various data.

[0118] The above-described implementation can certainly be used in television devices that use OLED displays, but it can also be used in electronic devices such as smartphones and personal computers.

[0119] Several embodiments of the application have been described; however, these embodiments are presented as examples and are not intended to limit the scope of the application. These new embodiments can be implemented in various other forms, and various omissions, substitutions, and modifications can be made without departing from the spirit of the application. These embodiments and their variations are included in the scope and spirit of the application, and are included in the technical solutions described in the claims and their equivalents. Furthermore, in the case where the constituent elements of the protected solution are expressed separately, or in combination, or in combination, these also fall within the scope of this application. Additionally, multiple embodiments can be combined, and embodiments formed by such combinations also fall within the scope of the application. Furthermore, the accompanying drawings, for the purpose of clarity, schematically show the width, thickness, shape, etc., of various parts compared to the actual form.

Claims

1. A video display method, wherein, The frame corresponding to the input video signal is divided into multiple blocks. Detect static image blocks between the beginning and end of the timeline of the frame. Calculate the grayscale values ​​of the static image blocks, divide them into defined ranges, and group adjacent static image blocks according to these ranges. The weighting of the coefficients is changed based on the area of ​​the grouped regions. The level of the displayed video signal is controlled based on the weighted coefficients.

2. A video display method, wherein, The frame corresponding to the input video signal is divided into multiple blocks. Detect static image blocks between the beginning and end of the timeline of the frame. Calculate the grayscale values ​​of the static image blocks, divide them into defined ranges, and group adjacent static image blocks according to these ranges. The weighting of the coefficients is changed according to the shape of the grouped regions. The level of the displayed video signal is controlled based on the weighted coefficients.

3. The video display method according to claim 1 or 2, wherein, Furthermore, a coefficient switcher is used to prepare multiple ranges of coefficients as said coefficients, and the coefficients of said multiple ranges can be switched.

4. The video display method according to claim 1 or 2, wherein, The level of the video signal is controlled by controlling the brightness level or controlling the RGB signal.

5. A video display device, wherein, include: A segmenter that divides the frame corresponding to the input video signal into multiple blocks; A detector that detects static image blocks between the beginning and end of the frame's timeline; A static image region calculator calculates the grayscale value of the static image block, divides it into a specified range, and groups adjacent static image blocks according to the range; An area calculator that adjusts the weighting of coefficients based on the area of ​​the grouped regions; as well as A coefficient calculator that controls the level of the displayed video signal based on the weighted coefficients.

6. A video display device, wherein, include: A segmenter that divides the frame corresponding to the input video signal into multiple blocks; A detector that detects static image blocks between the beginning and end of the frame's timeline; A static image region calculator calculates the grayscale value of the static image block, divides it into a specified range, and groups adjacent static image blocks according to the range; A shape detector that adjusts the weighting of coefficients based on the shape of the grouped regions; as well as A coefficient calculator that controls the level of the displayed video signal based on the weighted coefficients.

7. The video display device according to claim 5 or 6, wherein, The coefficient calculator that controls the level of the video signal controls the brightness level or the RGB signal.

8. A non-volatile storage medium storing a program for video display, wherein, The video display is processed by the program as follows: A control signal is output to the image brightness correction circuit. This control signal is used to reduce the level of the image signal in static image areas. The frame corresponding to the input video is divided into multiple blocks. Detect static image blocks between the beginning and end of the timeline of the frame. Calculate the grayscale values ​​of the static image blocks, divide them into defined ranges, and group adjacent static image blocks according to these ranges. The weighting of the coefficients is changed based on the area of ​​the grouped regions. The level of the displayed video signal is controlled based on the weighted coefficients.

9. A non-volatile storage medium storing a program for video display, wherein, The video display is processed by the program as follows: A control signal is output to the image brightness correction circuit. This control signal is used to reduce the level of the image signal in static image areas. The frame corresponding to the input video signal is divided into multiple blocks. Detect static image blocks between the beginning and end of the timeline of the frame. Calculate the grayscale values ​​of the static image blocks, divide them into defined ranges, and group adjacent static image blocks according to these ranges. The weighting of the coefficients is changed according to the shape of the grouped regions. The level of the displayed video signal is controlled based on the weighted coefficients.

10. The non-volatile storage medium according to claim 8 or 9, wherein, The level of the video signal is controlled by controlling the brightness level or controlling the RGB signal.

Images